Project Summary/Abstract For ovarian cancer (OvCa), only 27% of women diagnosed at advanced stages survive 5 years, yet more than 90% of patients survive when diagnosed at an earlier stage. Therefore, there is an urgent need for new non- invasive technologies capable of rapidly diagnosing ovarian cancers (OvCa) in early stage. Fortuitously, all cells (and tumor cells to a greater extent) expel nanoscale vesicles that are directly reflective of the biological state of their parent cells. A subset of circulating EVs known as exosomes are composed of biomolecules spanning the range of lipids, proteins, genes, and more, and hold great potential for the diagnosis and prognosis of cancer. Yet current methods for phenotyping biofluids according to detection of tumor-associated exosomes (TEXs) are not meeting clinical standards and fail to precisely capture particle to particle heterogeneity. We propose to develop a new nanoplasmonics-based technology for sensitive detection of cancer-related exosome bio-signatures enabled by multiplexed surface-enhanced Raman spectroscopy, that we call ExoSERS. Our approach encompasses three aims devised to realize the ExoSERS platform. Aim 1 outlines development of a new class of Raman-active ligands to serve as the molecular barcodes. This aim encompasses the design and synthesis of polyyne-based ligands designed to confer Raman spectroscopic encoding and also initiate a silane coating to form a protecting shell around a nanoplasmonic core. Aim 2 describes the synthesis and optimization of nanoplasmonic core-shell structures that will be well-suited to binding EVs. An inner gold core structure yields plasmonic enhancement, while the outer silica shell permits long-term stability and a convenient surface for covalent decoration with exosome and cancer-specific surface marker targeting agents. Aim 3 comprises validation of the platform?s feasibility to profile human OvCa patient plasma, including machine learning approaches to type cancers using the barcoded approach. Endpoints of platform characterization will be statistical validation of exosome detection efficiency, minimal sample volume needed, ease of utilization, and low cost. Several quantitative milestones have been proposed to gauge our progress and provide deliverables to the larger diagnostic and circulating biomarker communities.